Generating and Displaying Customer Commitment Framework Data

ABSTRACT

Systems and methods for generating and displaying customer commitment framework data are provided. Exemplary methods for determining the shareability of online content may include obtaining, via a digital intelligence system, customer experience data regarding any of a product, a brand, and customer responses for a first entity, as well as periodically calculating, via the digital intelligence system, customer commitment framework data from the customer experience data, and generating a customer commitment dashboard that comprises a graphical representation of the customer commitment framework data.

CROSS-REFERENCES TO RELATED APPLICATIONS

This Non-Provisional U.S. Patent Application is a continuation of U.S. patent application Ser. No. 14/794,776 filed on Jul. 8, 2015 and titled Generating and Displaying Customer Commitment Framework Data, which is a continuation of U.S. patent application Ser. No. 13/767,832, filed on Feb. 14, 2013 and titled Generating and Displaying Customer Commitment Framework Data (issued as U.S. Pat. No. 9,123,055 on Sep. 1, 2015), which is a continuation-in-part of U.S. patent application Ser. No. 13/587,789, filed on Aug. 16, 2012 and titled Customer Relevance Scores and Methods of Use (issued as U.S. Pat. No. 8,793,154 on Jul. 29, 2014), which claims the priority benefit of U.S. Provisional Application No. 61/525,041, filed on Aug. 18, 2011 and titled New Sharing and Recommendation Tracking Method. This application also relates to U.S. Provisional Patent Application No. 61/675,784, filed on Jul. 25, 2012 and titled Product Cycle Analysis Using Social Media Data, as well as U.S. Provisional Patent Application No. 61/606,326, filed on Mar. 2, 2012 and titled Product Cycle Analysis Using Social Media Data. All of the aforementioned disclosures are all hereby incorporated by reference herein in their entireties including all references cited therein.

FIELD OF THE PRESENT TECHNOLOGY

The present technology relates generally to generating and displaying customer commitment framework (CCF) data. Dashboards may be provided, which include representations of CCF data, such as graphs, tables, or other visual formats that provide users with views of CCF data in a manner that allows for real-time course-correction of programs and processes. Exemplary types of CCF data include product commitment scores (PCS), brand commitment scores (BCS), and customer response scores (CRS), which are each indicative of various aspects of a customer experience and journey analysis.

BACKGROUND

Social media communications provide a wealth of information regarding the purchasing behaviors and interests of consumers. While this information is voluminous, it is often difficult to categorize and translate this information into meaningful and actionable information that may be utilized by a company to improve their products, advertising, customer service, and the like.

SUMMARY OF THE DISCLOSURE

According to some embodiments, the present technology may be directed to a method for determining the shareability of online content. The method may comprise: (a) obtaining, via a digital intelligence system, customer experience data regarding any of a product, a brand, and customer relevance for a first entity; (b) periodically calculating, via the digital intelligence system, customer commitment framework data from the customer experience data; and (c) generating a customer commitment dashboard that comprises a graphical representation of the customer commitment framework data.

According to other embodiments, the present technology may be directed to systems for determining the shareability of online content. These systems may comprise: (a) one or more processors; and (b) logic encoded in one or more tangible media for execution by the one or more processors and when executed operable to perform operations comprising: (i) obtaining, via a digital intelligence system, customer experience data regarding any of a product, a brand, and customer relevance for a first entity; (ii) periodically calculating, via the digital intelligence system, customer commitment framework data from the customer experience data; and (iii) generating a customer commitment dashboard that comprises a graphical representation of the customer commitment framework data.

According to additional embodiments, the present technology may be directed to non-transitory computer readable storage mediums having a computer program embodied thereon. The computer program is executable by a processor in a computing system to perform a method that includes the steps of: (a) obtaining, via a digital intelligence system, customer experience data regarding a first entity; (b) periodically calculating, via the digital intelligence system, customer commitment framework data from the customer experience data; and (c) generating a customer commitment dashboard that comprises a graphical representation of the customer commitment framework data.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain embodiments of the present technology are illustrated by the accompanying figures. It will be understood that the figures are not necessarily to scale and that details not necessary for an understanding of the technology or that render other details difficult to perceive may be omitted. It will be understood that the technology is not necessarily limited to the particular embodiments illustrated herein.

FIG. 1 is a block diagram of an exemplary digital intelligence system.

FIG. 2 is a block diagram of an exemplary digital intelligence application for use in accordance with the present technology;

FIG. 3 illustrates exemplary keyword matrices for categorizing social media conversations;

FIG. 4 is an exemplary customer commitment dashboard illustrating product commitment score data;

FIG. 5 is an exemplary customer commitment dashboard illustrating product commitment score data as well as brand commitment score data;

FIG. 6 is an exemplary customer commitment dashboard illustrating product commitment score data as well as brand commitment score data and customer relevancy score data;

FIG. 7 is an exemplary customer commitment dashboard illustrating product commitment score (PCS) data displayed according to segmentation;

FIG. 8 is an exemplary customer commitment dashboard illustrating PCS segmentation and customer journey data;

FIG. 9 is an exemplary customer commitment dashboard illustrating PCS segmentation and segmentation characteristics;

FIG. 10 is an exemplary customer commitment dashboard illustrating a time-based and graphical PCS data analysis;

FIG. 11 is an exemplary customer commitment dashboard illustrating a time-based and graphical analysis of customer journey data;

FIG. 12 is an exemplary customer commitment dashboard illustrating a time-based and graphical analysis of experience scores relative to product awareness; and

FIG. 13 is a block diagram of an exemplary computing system for implementing embodiments of the present technology.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

While this technology is susceptible of embodiment in many different forms, there is shown in the drawings and will herein be described in detail several specific embodiments with the understanding that the present disclosure is to be considered as an exemplification of the principles of the technology and is not intended to limit the technology to the embodiments illustrated.

It will be understood that like or analogous elements and/or components, referred to herein, may be identified throughout the drawings with like reference characters. It will be further understood that several of the figures are merely schematic representations of the present technology. As such, some of the components may have been distorted from their actual scale for pictorial clarity.

Generally speaking, the present technology is directed to systems, methods, and media to generate and display customer commitment framework data. Broadly, the customer commitment framework (CCF) empowers organizations to optimize their customer experience with a data-driven approach to decision-making. CCF provides real-time predictive measures and robust insights to strengthen customer commitment around the three exemplary customer journeys, including Shopping, Sharing, and Advocacy.

The present technology provides intuitive dashboards, known as customer commitment dashboards (CCDs), which may advantageously be used to drive real-time course-correction of programs and processes. Because CCD models CCF data, which includes customer experience and customer journey data, product and brand scores may be benchmarked and compared to competitors, as well as an industry average, providing the ability to leverage best practices in any given industry vertical.

The present technology may calculate the aforementioned product and brand scores that indicate how well received a product or brand is amongst consumers. These scores may also be used to predict buying behaviors and target consumers based upon their position within a product cycle. That is, scores may be calculated that represent consumer experiences across many phases of a product cycle (e.g., development, launch, updating, phase out, and the like).

The terms “customer experience data” as used throughout this document refers to customer experience and/or customer journey data gathered from a wide variety of source including, but not limited to data gathered from social networking platforms, as well as news sources, forums, blogs, and so forth. One of ordinary skill in the art will appreciate that any digital data source that provides customer experience or customer journey data, as described herein, may likewise be utilized in accordance with the present disclosure. An “author” may include any customer or potential customer that has generated content that expresses an opinion about a product or service, especially content that relates to a customer experience or a customer journey.

Many of the examples provided herein reference the analysis of conversations and content occurring via social media platforms. It will be understood that these examples are non-limiting and the CCF analysis methods described herein may be equally applied to content occurring via other content sources, such as those mentioned above.

Additionally, the terms “a product” may be claimed or referred to as both products and/or services provide by an entity.

Generally, CCF data may be determined from the gathered customer experience data. In some instances, the CCF data may include various scores, such as PCS, BCS, and/or CRS, which are real-time predictive measures data, which may be used as a means to strengthen customer commitment.

Prior to calculating various scores that indicate how well received a product, customer experience, and/or customer journey is amongst consumers, the present technology may evaluate customer experience data from authors and categorize conversations or other content. In some instances, conversations may be categorized as falling within a product commitment score domain, a brand commitment score domain, and/or a customer relevance score domain. Generally speaking, conversations or content may be categorized by evaluating keywords included in the conversations, and more specifically based upon a frequency of keywords included in these conversations.

In some instances, BCS may be utilized to determine a consumer's emotional connection to a brand. Exemplary emotions associated with the BCS may comprise, but are not limited to, hopefulness, attraction, and/or devotion. These emotions may be tied to segments of the product cycle, such as understand, learn, and commit—customer engagement levels with a particular brand.

The CRS allows marketers to determine the shareability of content. By making content more shareable, marketers can increase website traffic, which may in turn, drive consumer behaviors within the product cycle, such as buying. The CRS may quantify the shareability of content and may be used as a benchmark for comparing the effectiveness of content in driving commercial activity.

Customer relevance scores (CRS) may quantify how likely it is that a customer or social network user will share a particular piece of content, such as a video, an article, a website, or other online content within the context of a social network. The CRS gauges the shareability of content. Because it can be demonstrated with empirical evidence that shared content increases revenue more than passive or unshared content, increasing the CRS for content may result in a corresponding increase in revenue attributable to the content. In sum, the CRS may be used to quantify the value of the content, based upon its shareability.

Generally speaking, the present technology may be utilized to determine the shareability of online content. Additionally, the present technology may be utilized to evaluate how and why content is shared. Content that is more frequently shared may be analyzed to determine various elements that make the content shareable, such as narrative, thematic, and underlying message elements. Additionally, the present technology may be used to create code frames from frequently shared content. These code frames (similar to templates) may be applied to other content to increase the shareability of the content. Shareable content may be created from scratch using these code frames.

Advantageously, the present technology may track customer experience data for online content, such as sharing of the content, downloading of the content, uploading of the content, conversations relating to the content, and so forth. Using this customer experience data, the present technology may determine the shareability of the content. When content sharing is quantified, the metrics gathered may be utilized to increase key brand metrics, understand and quantify what makes certain content shareable, increase advertising recall, increase correct branding of advertising, increase brand consideration, increase brand recommendation, increase brand favorability, and increase enhanced resonance and acceptance of campaign messages.

In some instances, the present technology may mathematically quantify consumer sentiment relative to a product. Moreover, the consumer sentiment may be extracted from an analysis of content included in social media messages and conversations. Additionally, the portion of the product cycle in which the consumer is currently participating may be determined by an analysis of the words included in their customer experience data. Therefore, consumer sentiment regarding a product may be determined relative to a time frame associated with at least one phase of a product cycle for the product.

In some exemplary embodiments, the scores calculated by the present technology may be based upon data included in social media messages of authors (e.g., consumers posting messages on social networks). Thus, customer experience data obtained from various social media sources may provide valuable and actionable information when transformed by the present technology into various metrics. Each of the metrics/scores/values calculated by the present technology is described in greater detail herein.

Referring to FIG. 1, the present technology may be implemented to collect and evaluate customer experience data for analysis as customer commitment framework data. The present technology may be facilitated by a digital intelligence system 100, hereinafter “system 100” as shown in FIG. 1. The system 100 may be described as generally including one or more web servers that may communicatively couple with client devices such as end user computing systems. For the purposes of clarity, the system 100 is depicted as showing only one web server 105 and one client device 110 that are communicatively coupled with one another via a network 115. Additionally, customer experience data gathered from various sources (not depicted in FIG. 1) may be stored in database 120, along with various scores, values, and the corresponding data generated by the web server 105, as will be discussed in greater detail below.

It is noteworthy to mention that the network 115 may include any one (or combination) of private or public communications networks such as the Internet. The client device 110 may interact with the web server 105 via a web based interface, or an application resident on the client device 110, as will be discussed in greater detail herein.

According to some embodiments, the system 100 may include a cloud based computing environment that collects, analyzes, and publishes datasets. In general, a cloud-based computing environment is a resource that typically combines the computational power of a large grouping of processors and/or that combines the storage capacity of a large grouping of computer memories or storage devices. For example, systems that provide a cloud resource may be utilized exclusively by their owners, such as Google™ or Yahoo! ™; or such systems may be accessible to outside users who deploy applications within the computing infrastructure to obtain the benefit of large computational or storage resources.

In some embodiments according to the present technology, the cloud may be formed, for example, by a network of web servers such as web servers 105 with each web server (or at least a plurality thereof) providing processor and/or storage resources. These servers may manage workloads provided by multiple users (e.g., cloud resource consumers or other users). Typically, each user places workload demands upon the cloud that vary in real-time, sometimes dramatically. The nature and extent of these variations typically depend on the type of business associated with the user.

The system 100 may be generally described as a particular purpose computing environment that includes executable instructions that are configured to generate and display customer commitment framework data.

In some embodiments, the web server 105 may include executable instructions in the form of a digital intelligence application, hereinafter referred to as “application 200” that collects and evaluates customer experience data used in various customer commitment framework data analyses. FIG. 2 illustrates and exemplary schematic diagram of the application 200.

The application 200 is shown as generally comprising an interface module 205, a data gathering module 210, a Product Commitment Score (PCS) module 215, a consumer experience module 220, a segmentation module 225, a CRS module 235, and a code framing module 240. It is noteworthy that the application 200 may include additional modules, engines, or components, and still fall within the scope of the present technology. As used herein, the term “module” may also refer to any of an application-specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. In other embodiments, individual modules of the application 200 may include separately configured web servers.

Generally speaking, the interface module 205 may generate a plurality of graphical user interfaces that allow end users to interact with the application 200. These graphical user interfaces may allow end users to input information that is utilized by the system 100 to capture and analyze customer experience data. The information input by end users may include product information for products they desire to evaluate, the product cycle or a portion of the product cycle of interest, the type of consumers or messages they desire to analyze, and so forth.

Initially, the data gathering module 210 may be executed to obtain customer experience data from one or more digital mediums, such as websites, blogs, social media platforms, and the like. End users may establish profiles that define what types of customer experience data are to be gathered by the data gathering module 210. For example, a software developer may desire to gather customer experience data regarding consumer sentiment for a particular application.

The data gathering module 210 may evaluate customer experience data for keywords, groups of keywords, or search queries that are utilized to search for conversations or messages that include these keywords.

FIG. 3 illustrates various matrices that may be used to semiotically evaluate conversations or other content. For example, if a conversation has a predominate number of keywords that fall in the customer relevance score (CRS) matrix, the conversation may be categorized as falling within the CRS domain. Thus, a CRS equation may be utilized to calculate a CRS for the social media conversation, as will be discussed in greater detail.

Exemplary PCS core keywords are shown in matrix 305, while exemplary BCS core keywords are included in matrix 310. Exemplary CRS core keywords are included in matrix 315, which includes column 320 of Interested, column 325 of Connected, and column 330 of Sharing. Each of these columns may be associated with a shareability classification in some embodiments. Thus, keywords in a conversation may place the conversation into one or more of these classifications, namely Interested, Connected, and/or Sharing, respectively.

For example, if a conversation included the words sharing and endorsing, which are included in the Sharing column 330, the conversation may be classified within the Sharing classification. The conversation may be placed into more than one classification if the system detects keywords present in the Interested or Connected columns. In some instances, the conversation may be classified by a predominance of classifying words in the conversation. Thus, if the conversation includes a predominate number of Interested keywords, the conversation may be classified as Interested. In some embodiments, these classifications may also be weighted such that the inclusion of a predetermined number of Sharing keywords automatically causes the conversation to be classified with the Sharing classification, regardless of how many other Interested or Connected keywords are present in the conversation.

In accordance with the present disclosure, selection of customer experience data may be influenced by the specific types of behaviors that a merchant is attempting to quantify. In other embodiments, the data gather module 210 may analyze the customer experience data to determine where within the product cycle a consumer currently resides—for example, in the awareness, interest, desire, or action phases. Awareness may be inferred from conversations that discuss any of the three key drivers of the product cycle (e.g., learn, try, buy, and the like). Interest in a product may be a strong indicator that a consumer has gone beyond being simply aware of a product. When consumers expressing a desire to purchase a product it may be inferred to be a strong indicator that consumers are considering a product for purchase. Additionally, when consumers indicate an active intent to purchase a product, it may be inferred that the consumer is strongly progressing along the buying cycle.

In some embodiments according to the present technology, the data gathering module 210 may obtain customer experience data from specific types of consumers, and in additional embodiments, based upon where the consumers are positioned within the product cycle, such as those within the research phase. That is, the customer experience data for a set of consumers may be monitored because they are actively researching products to purchase.

The data gathering module 210 may utilize a conversation matrix to obtain relevant customer experience data. The data gathering module 210 may employ the conversation matrix to search and capture relevant customer experience data from various digital platforms. The search terms and matrices utilized by the data gathering module 210 may be updated if the data gathering module 210 fails to obtain sufficient data, or if the data that is obtained is inaccurate.

Product Commitment Scores

The PCS module 215 may be executed to calculate various types of PCS values that aid merchants in determining the commitment level of consumers to a particular product. Additionally, the PCS value may be utilized as a leading indicator that may be utilized to predict consumer behavior relative to a particular product or service. For example, the PCS value may be used to predict how well a particular product will be received by consumers. Moreover, the PCS value may be used to predict the likelihood that a product will be purchased and if consumers will remain committed to the product throughout the lifecycle of the product. In some non-limiting examples, if the product includes software, the product lifecycle may include conception, product launch, and eventual upgrade of the software by consumers.

In some embodiments, the PCS value may represent the difference between the number of positive research, trial, and purchase messages and the number of negative research, trial and purchase messages. Again, these messages may include social media messages and may be obtained by the data gathering module 210 from evaluating one or more social media platforms. More specifically, these messages or conversations have been previously categorized as belonging to, or being associated with, the PCS domain.

It is noteworthy that the PCS module 215 may calculate individual PCS values at a specific consumer (e.g., author) level. Adjustments and weighting of consumer level PCS values may also be performed by the PCS module 215.

For example, each consumer may contribute to the overall PCS value to the degree of their relative authority. That is, the PCS module 215 may account for a consumer's influence relative to the total influence of all consumers having at least one research, trial, or purchase conversation relative to a particular product.

The PCS module 215 may also adjust consumer level PCS values to account for each consumer's influence relative to the influence of all consumers having at least one research, trial, or purchase message. That is, the more influential a consumer is, the more weight is attributed to the consumer's conversations. Influence may be inferred because the consumer has a large social network or because the consumer is an expert in the product field.

The overall PCS value may generally comprise one or more summation consumer level PCS value. In additional embodiments the overall PCS value (and consumer level PCS values) may comprise a summation of, for example, three different component values such as a research value, a trial value, and a purchase value, where each of these values may be calculated separately. These three values represent the phases of the product cycle.

In general, each of the three component values may each include a summation of seven different sentiment values. Conceptually, the seven sentiment values exist on a continuum where the first sentiment value indicates a very negative sentiment, and the seventh sentiment value indicates a very positive sentiment. The second through sixth sentiments fall somewhere in between. The distributions of messages/conversations along the spectrum of sentiment values may indicate the success of the product at the different phases of the product cycle.

In some embodiments, messages that are the most positive (sentiment score of seven, for example) may receive the most points, whereas the least positive (sentiment score of five, for example) may receive the least amount of positive points. The most negative conversations (sentiment score of one) may receive the greatest number of negative points. Conversations being the least negative (sentiment score of four) may receive the fewest negative points.

As mentioned briefly above, consumer level PCS may also be weighted. For example, a consumer having 100% most positive conversations in the research, trial and purchase categories should get the maximum score of 100. As such, the weight for sentiment seven=100/3=+33.33.

Likewise, a consumer having 100% most negative conversation in the research, trial and purchase categories should get the minimum score=−100. As such, the weight for sentiment 1=−100/3=−33.33.

Consumers that have less negative conversations than sentiment one, a decrease in penalization points of −33.33 may be seen, respecting the original weighting. As consumers have less positive conversations their reward points may be reduced to respect the original weighting.

In sum, the PCS module 215 may consider not only the aggregate number of conversations in each phase of the product cycle, but the sentiment level associated with each conversation. Additionally, the sentiment for each conversation may be weighted based upon consumer characteristics (e.g., mood, influence, etc.). Moreover, the conversations may further be weighted by the authority level of the consumers associated with the conversations. The final PCS (either overall or consumer level) may then be indexed from zero to 100, for example, where 100 indicates that the product scores perfectly through the product cycle or at least one phase of the product cycle.

The present technology may be adapted to adjust the consumer level and overall PCS values based upon various factors. For example, a value calculated for the sentiment of a message may be adjusted for the consumer's general mood, such as when it is known that the consumer is always positive or almost always skeptical and/or negative. In other instances the PCS values may be adjusted based upon the importance of a particular message to the sale of a product or service.

While many methods for calculating and weighting PCS have been disclosed one or ordinary skill in the art will appreciate that other algorithms and weighting methodologies that may be utilized to quantify and predict consumer sentiment and buying behaviors for product cycles are likewise contemplated for use in accordance with the present technology.

PCS values may also be utilized to benchmark a particular product against a competing product. For example, a PCS value for a navigation software application for a first merchant may be compared against a PCS value for similar navigation software from a competing merchant. The PCS value may provide actionable information that allows the first merchant to modify their marketing, consumer service, and/or product features to increase their PCS value. It is noteworthy to mention that PCS values may be generated for merchants at specific intervals, such as daily, weekly, monthly, or quarterly.

According to some embodiments, the consumer experience module 220 may be executed to evaluate portions of the consumer journey (e.g., product cycle) relative to a product. Generally speaking, consumer experience values may comprise mathematical representations of customer experience data at specific point in time (or a specific time period) along the product cycle. In some instances, the consumer experience scores may include the three PCS component values (e.g., research value, trial value, and purchase value) described above, but analyzed relative to a particular time frame. Therefore, the consumer experience values may be described as more granular and temporally focused portion of the PCS (either intermediate or overall).

Consumers may be previously identified by the data gathering module 210, for example, by identifying consumers in certain types of survey data. Various scores may be generated by the consumer experience module 220 that represent different consumer experiences. These scores/values may be utilized by merchants to improve their products and/or marketing campaigns.

Using the consumer experiences scores, a merchant may explore more detailed metrics regarding the touchpoints surrounding a product. In some instances, the consumer experience scores may be generated by conducting a more detailed evaluation of customer experience data relative to the calculation of a PCS. Therefore, the conversation matrices employed by the data gathering module 210 may be modified (e.g., may include greater detail) to capture more specific portions of digital conversations/messages across phases of the product cycle. The consumer experience module 220 may also generate optimal consumer journey models that enable merchants to plan effective product development and marketing strategies, while also allowing for course correction when products or marketing fail to produce acceptable consumer experiences.

According to some embodiments, the segmentation module 225 may be executed to determine and develop actionable priorities tailored to specific consumer types. The segmentation module 225 may cluster consumers based on a variety of factors using a segmentation model that considers product cycle components and likelihood of purchasing a product. The segmentation module 225 may utilize the social data gathered by the data gathering module 210. Additionally, the segmentation module 225 may generate feedback for consumer segments in near real-time, specifically for consumers that are the most (and alternatively the least) likely to purchase a particular product.

In some embodiments, utilizing the data gathering module 210, consumer customer experience data may be obtained from groups of consumers engaged in traditional marketing or consumer research activities. Consumers may be queried for a social networking identifier (e.g., handle, profile, username, etc.) such that the data gathering module 210 may collect customer experience data for that consumer. When customer experience data is obtained, the segmentation module 225 may link or correlate the customer experience data with primary research data, such as data obtained from traditional marketing or consumer research activities. The segmentation module 225 may evaluate customer experience data of the consumer to determine if the consumer is acting in correspondence with the research data gathered about the consumer. Moreover, the segmentation module 225 may also determine if the consumer is influencing other consumers with their social media conversations. The segmentation module 225 may also used the combined data sets to generate models that allow the segmentation module 225 to predict which social media conversations that should be tracked to glean the most accurate and relevant information about the consumer.

In other embodiments, the segmentation module 225 may utilize the correlated group consumers into categories based upon various factors. For example, very influential consumers who focus on superior customer service may be clustered into a consumer segment.

The segmentation module 225 may segment or cluster the customer experience data based upon the content of the social media conversations. For example, the segmentation module 225 may evaluate a group of social media messages and determine that two thirds of the consumer's desire superior consumer service, whereas only five percent desire an aesthetically pleasing website. Again, the clustering, as with sentiment analysis, may be conducted based upon keywords included in the social data. As with PCS values and consumer experience values, the segmentation module 225 may determine the segmentation of customer experience data based upon certain algorithms, mathematical, and/or statistical methodologies. According to some embodiments, the segmentation module 225 may employ statistical methodologies such as clustering ensembles. The clustering of consumers allows the merchant to direct more resources to consumer service efforts and away from website development. As consumer sentiments change, so may the segmentation, and thus the priorities of the merchant.

Brand Commitment Scores

Based upon the categorization of the social media conversation as being within the BCS domain, the BCS module 230 may be executed to calculate a BCS for a social media conversation. According to some embodiments, the BCS quantifies brand affinity for a particular consumer or group of consumers. The BCS may also quantify the consumer's emotions regarding the brand. The BCS may provide a metric, which allows merchants to build relationships between customers and brands.

In some embodiments, the BCS is a composite calculation that encompasses the understand, explore, and commit segments of the brand affinity journey. The BCS relates to the brand affinity journey inasmuch as the understand segment of the brand affinity journey is associated with hopefulness, the explore segment of the brand affinity journey is associated with attraction, and the commit segment of the brand affinity journey is associated with devotion. Keywords conveying these emotions may be used to categorize a social media conversation as falling within the brand commitment domain.

In greater detail, the hopefulness emotion attempts to quantify what is important to a customer. Common keywords associated with hopefulness may comprise, but are not limited to hope, expect, optimistic, and so forth. Using this metric, merchants may be able to align expectations of their consumers with their brand. Merchants may tailor their branding and/or marketing to set a level of expectation regarding their products and long-term relationship with customers. The branding of an organization or products may be utilized to build and maintain an emotional connection with consumers which may be leveraged to drive sustained purchasing behavior for products and advocacy in content engagement.

The attraction emotion attempts to quantify if the brand properly reflects who their customers are. Common keywords associated with attraction may comprise, but are not limited to excited, admire, appeal, and so forth. Using this metric, merchants may be able to identify reconciliation when needed. Merchants may tailor their branding and/or marketing to ensure that their products are being advertised and/or branded in accordance with the needs of their customers. These needs may comprise reputation, quality, popularity, and so forth.

The devotion emotion attempts to quantify how deeply the consumer is committed to the brand. Common keywords associated with devotion may comprise, but are not limited to love, loyal, trust, and so forth. Using this metric, merchants may be able to identify a relationship status between a brand and a consumer. The more devoted the customer is to the brand, the more committed the customer will be to purchasing the product associated with the brand. Merchants may wish to tailor their branding or marketing to drive up customer devotion and identify consumers with lagging commitment.

Because these metrics and resultant BCS may be tracked over time and per author, the merchant may determine how changes in marketing and/or branding strategies affect these different consumer emotions. BCS may be calculated for groups or consumer segments such as demographic, psychographic, or other common consumer segmentations that would be known to one of ordinary skill in the art with the present disclosure before them.

An exemplary algorithm (Equation A) for calculating a BCS for a social media conversation is provided below:

Σ(Ar/ΣAr)*Cw*Sa  (Equation A)

where an author rank score Ar is first calculated for each of a group of authors. The group of authors may include the known customers or alternatively, a subgroup of customers. An author rank may be calculated by determining an influence for an author. The influence of an author may be determined, for example, by a number of connections for the author (e.g., followers, contacts, etc.). The social status of an author may also be considered. For example, an influential celebrity may have their conversations ranked more highly than an average consumer in some embodiments.

Once an author rank score has been calculated for each author in the group of authors, the author rank score for the author of the comment may be divided by a sum of the author rank scores for each author in the author group to generate an adjusted author rank score. The author rank scores and/or adjusted author rank score may be calculated over a given period of time, relative to a particular product. Thus, BCS may be calculated over time to provide merchants with indices or metrics that quantify how well their branding efforts are being received by consumers.

Next, a component weight Cw for the conversation may be multiplied with the adjusted author rank score. The component weight may comprise previously established scaling factors for each stage of the product cycle. For example, the understand/hopefulness scaling factor may be approximately 0.15, whereas the explore/attraction scaling factor may be approximately 0.25. Additionally, the commit/devotion scaling factor may be approximately 0.6. Thus, in some embodiments, the most important scaling factor for component weight relative to the BCS is the commit/devotion scaling factor. Advantageously, the commit/devotion scaling factor may be attributed more weight because the BCS attempts to determine a brand commitment level for consumers. Therefore, devotion may be strongly correlated to brand commitment, whereas hopefulness and/or attraction are less likely to be indicative of brand commitment, although they may be contributory to some degree.

As mentioned previously, the component weighting for each of these three scaling factors may be determined based upon empirical evidence, such as the evaluation of social media conversations of trustworthy authors. For example, a plurality of conversations gathered from various trustworthy consumers may be utilized as the basis for setting the weight of individual scaling factors.

Additionally, the BCS may be weighted based upon consumer sentiment Sw as described in greater detail herein. Thus, the system may calculate a sentiment weight for consumers who are consistently positive, negative, neutral, or otherwise amenable to sentiment categorization. This consumer sentiment Sw may be utilized to alter the BCS for a particular conversation. For example, an author who is consistently negative may have their BCS adjusted based upon a negative conversation. In contrast, the consistently negative author may have their BCS adjusted upwardly if they express devotion within their conversation for a particular brand or product, as it would be unexpected for the author to provide a positive conversation.

Customer Relevance Scores

In some embodiments, based upon the categorization of the social media conversation as being within the CRS domain, the CRS module 235 may be executed to calculate a CRS for online content. According to some embodiments, the CRS may quantify how likely it is, or will be, that a customer or social network user will share a particular piece of content, such as a video, an article, a website, or other online content within the context of a social network.

For example, end users such as marketers may desire to obtain shareability metrics for online content. The data gathering module 210 may be executed to gather customer experience data regarding the online content. For example, the data gathering module 210 may gather metrics regarding sharing, downloading, uploading, posting, linking, referencing, liking, tagging, or other similar actions for online content that would occur within the context of a social network. The data gathering module 210 may also gather conversations, commentary, blog posts, articles, or other textual information associated with online content. For example, the data gathering module 210 may capture a comment thread associated with a video.

Next, the CRS module 235 may evaluate the customer experience data to classify the conversations. Shareability classification may be performed by determining if keywords included in the conversations about the online content fall within any of the columns of the CRS core matrix 315 of FIG. 3. Once the social media conversations have been classified, a CRS may be calculated for each conversation, a group of conversations for a particular author, or of all conversations for all authors in regard to the online content in question, and so forth.

An exemplary CRS may be calculated by the CRS module 235 using Equation A provided herein. That is, the adjusted author rank score may be calculated in a similar manner described herein with regard to the BCS. In contrast with the method for calculating the BCS, the CRS may contemplate different component weights Cw for the conversations. That is, the component weights for the CRS may be associated with the shareability classifications described herein such as interested, connected, and sharing. Whereas the BCS related to the understand, explore, commit product cycle segments with hopefulness, attraction, and devotion, the CRS relates to the understand, explore, and commit product cycle segments with interested, connected, and shared.

Thus, the component weight Cw for a conversation may comprise previously established scaling factors for each stage of the product cycle. For example, the understand/interested scaling factor may be approximately 0.15, whereas the explore/connected scaling factor may be approximately 0.25. Additionally, the commit/shared scaling factor may be approximately 0.6. Thus, the fact that the content has been shared is more relevant to the CRS than the keywords that indicate that the consumer is interested in the content. The connected shareability classification may consider how often the consumer mentions their social media connectivity and is therefore more related to shareability than words that simply connote an interest in the content.

As the conversations are evaluated for keywords and classified, the CRS module 235 may determine a shareability level for the online content. For example, if a predominate number of conversations include keywords that fall within the Interested classification, the CRS module 235 may indicate that the online content is likely to pique the interests of consumers.

In accordance with the weightings described herein, conversations that predominately involve keywords in the Interested classification will generate lower CRS relative to conversations that predominately involve keywords in the Connected classification in some embodiments. Therefore, conversations that predominately involve keywords in the Shared classification will have the highest relative CRS in some embodiments.

Again, the CRS may be calculated by the CRS module 235 at an author level. Additionally, an average CRS may be calculated for a plurality of authors having social media conversations about selected online content. Moreover, as with the BCS, the CRS may be corrected, weighted, adjusted, or otherwise modified based upon the sentiment score for a particular author.

In sum, Equation A may be employed by the PCS module 215, the BCS module 230, and/or the CRS module 235 to calculate the PCS, BCS, and CRS, respectively. The score calculated by the use of Equation A is dependent upon the initial classification of social media conversations as falling within the PCS, BCS, or CRS domains in some embodiments. Thus, Equation A may be used to calculate PCS for social media conversations falling with the PCS domain, while BCS and CRS may be calculated for conversations falling within the BCS and CRS domains, respectively.

Although not shown in Equation A, the CRS for online content may be augmented by web analytics regarding the online content. For example, the CRS module 235 may determine click counts, share counts, embeds, links, or other quantifiable ways online content may be shared within social networks. These analytics may be used to adjust the CRS. Social media conversations that generate relatively low CRS may be offset by the fact that the content is, in fact, shared more frequently than the conversations imply.

To increase the shareability of online content, the code framing module 240 may be utilized to generate a code frame that may be applied to online content having a relatively low CRS. For example, a code frame may be generated to increase the CRS of online content that falls within the Interested classification, described herein.

The content that generated the relatively high CRS may be evaluated by the code framing module 240 using a semiotic analysis to determine categories that define the shareability of content. With regard to semiotics utilized by the code framing module 240, a code or “frame” may be built to understand how various elements of online content work together to create meaning and trigger subconscious sharing impulses in consumers. These elements may be labeled as categories. Significant, but non-limiting categories that trigger subconscious sharing impulses may comprise a Narrative category may define the style of the content. A Theme category may define the type of story that the content is attempting to convey. An Underlying Message category may define the moral outcome or other message that the content is attempting to convey. Highly shareable content may engage the consumer on these various levels and success in these various levels may be determined by evaluating consumer response to content for these various categories.

Once the code frame has been generated by the code frame module 240 (also referred to as code framing module 240), online content having a relatively low CRS, which is similar to the type of content used to generate the code frame, may be applied to the content to increase the CRS for the content.

In some instances, a semiotic analysis may be performed on the online content with the low CRS to determine the narrative, theme, and underlying message categories for the online content. If the code frame module 240 can determine discrepancies between the code frame and the categories of the online content, the code frame module 240 may identify what categories need improvement. In other instances, marketers may utilize the code frame as a guideline for correcting defective content (e.g., content that is not being shared adequately). According to some embodiments, the code frame may be used by marketers to generate online content from scratch. Thus, the code frame may be used as a template to create content that is highly likely to be shared.

According to various exemplary embodiments, the system 100 may be configured to generate and display customer commitment framework data. Broadly, the customer commitment framework (CCF) empowers organizations to optimize their customer experience with a data-driven approach to decision-making. CCF provides real-time predictive measures and robust insights to strengthen customer commitment around the three exemplary customer journeys, including Shopping, Sharing, and Advocacy. With regard to Shopping, the system 100 may utilize CCF data informs and enables product and service strategy which foster awareness, feed trends, convert shopping into customers, and insure long-term commitment within target markets.

Indeed, spending money may be considered the ultimate commitment behavior, whether for the first time, buying more, or upgrading. The CCF data allows users to make marketing investments that lead customers along the product journey and closer to purchase through understanding the enablers and barriers to purchase. Responsive, intelligent marketing can influence the drivers behind purchasing. To support the journey and closing of shopping behaviors, the entire enterprise must be aligned with effective sales channels and supply chain to create the optimal customer experience. CCF data may be utilized by the system 100 to provide the user with visually appropriate and intuitive dashboards that present metrics (e.g., scores) in a manner that allows the user to determine instances where the closing of shopping behaviors may be increased.

With regard to Sharing, the system 100 may utilize and display CCF data, allowing the user to develop a content strategy for creating impactful and relevant content, leading to highly engaged customers. Advantageously, these engaged customers are more likely to share and amplify the impact of advertising content.

Indeed, effective content production and management are critical to ensure that messages and customer experience across all channels are relevant. Relevancy drives sharing and the CCF data generated and displayed by the system 100 can increase the likelihood of user endorsement of products and services.

With regard to Advocacy, the system 100 may utilize CCF data to inform users of their brand strategies, which may be used to create vocal advocates for products and services. In some embodiments, the system 100 may generate Customer Commitment Dashboards (CCDs), provide social data-derived KPIs and journey mapping, as well as customer segmentation. When coupled with human-led analysis and consulting, this system creates a deep and real-time understanding of your customer's experiences along their journey; as well as targets your organizational investments on what is most important to your most valuable customers.

It will be understood that the CCDs generated by the present technology may effectively model customer experience data, and provide product and brand scores, which may be benchmarked and compared to competitors, as well as an industry average, providing the ability to leverage best practices in any given industry vertical.

Generally, the system 100 may analyze CCF data to evaluate a customer journey with predictive scores that provide an understanding of the barriers and enablers in the customer journey to shopping, advocacy and sharing. Additionally, the system 100 may model social data to customer challenges and opportunities in real-time, enabling users to plan and react quickly, by providing predictive measures with appropriate diagnostics.

Additionally, the CCF data generated by the system 100 may be used to pinpoint both challenges and opportunities for enhancing the customer experience by applying CCF scores (PCS, CRS, BCS) to specific stages of the customer journey, suggesting which areas of an organization should be engage to increase customer response. In addition, in some embodiments the system 100 selectively targets CCF data and analyzes only those social media conversations that are indicative of a key customer journey, turning the “big” social dataset into targeted insights for effective course correction via the use of dashboards. Again, the system 100 may also model CCF data for brand and products against competitors CCF data to locate areas of competitive opportunity and highlight a competitor's best practices.

In some instances, the system 100 may provide statistical links between CCF scores (such as PCS, CRS, and BCS) and established scorecard metrics such as customer satisfaction (CSAT), net satisfaction score (NSAT), and net promoter score (NPS)—just to name a few. Such comparisons allow users to understand how these measures move together and impact each other to enable smarter decision-making and corporate scorecard improvement.

According to some embodiments, the system 100 may be programmed to obtain customer experience data from one or more social media platforms regarding a first entity, such as the user who desires to view CCDs regarding their CCF data. In some instances, periodic calculations of customer commitment framework data from the customer experience data are performed. As mentioned above, this CCF data may include, but is not limited to the PCS, BCS, and CRS scores described above, but may also include ancillary or complementary scores such as CSAT and the like. In some instances, the system 100 may calculate these scores at a given interval, such as every hour, or each day. These scores may be stored in a database (such as database 120 of FIG. 1), also referred to as a data laboratory.

Upon a user request, the system 100 may generate a customer commitment dashboard that comprises a graphical representation of the customer commitment framework data. While various types of representations of customer commitment framework data will be described in greater detail below, it will be mentioned that CCF data may be calculated for a plurality of market segments and in a plurality of languages. Thus, users that market their products in various markets, and to consumers in various countries, may use CCF data that may be used to evaluate and improve marketing within these markets and languages.

In some instances, the system 100 may calculate average scores for a plurality of industry verticals. It is noteworthy that an average score may include any of an average product commitment score, an average brand commitment score, and an average customer relevance score. These “average” scores represent an average of corresponding scores for competitors of the first entity. Thus, a user can compare their data against an aggregate/average score relative to their competition.

Generally, FIGS. 4-12 each illustrate exemplary graphical user interfaces in the form of customer commitment dashboards. These dashboards are merely exemplary and are representative of the many types of GUIs that may be generated by the system 100. Thus, one of ordinary skill in the art will appreciate that exact types of CCF data, and their arrangement into a CCD may depend upon the type of information that is relevant to the viewer. The system 100 may utilize benchmarking comparisons as the basis for determining how the data is displayed to the user, or when data is displayed to the end user. For example, if the system 100 detects highly discrepant PCS score(s), relative to an average PCS score for a company's nearest competitors, the system 100 may choose to display such data first, or highlight such data to draw the attention of the user.

FIG. 4 is an exemplary customer commitment dashboard 400 illustrating product commitment score data. The dashboard 400 is shown as comprising PCS quadrant graph 405, which includes data points for a plurality of competing products. The graph 405 is a function of PCS score values, which extend across the Y-axis of the graph, while product volume values extend across the X-axis of the graph. In this instances, the PCS number comprise year-to-date PCS scores averages for each product.

The dashboard 400 also includes a table representation 410 showing industry vertical CCF data, which includes average PCS scores for each vertical, a highest PCS score for each vertical, as well as a lowest PCS score for each vertical.

FIG. 5 is an exemplary customer commitment dashboard 500 illustrating product commitment score data as well as brand commitment score data. The dashboard 500 of FIG. 5 includes the same data as provided in FIG. 4, and additionally includes similar graphs and tables for BCS scores. FIG. 6 is an exemplary customer commitment dashboard 600 illustrating product commitment score data as well as brand commitment score data and customer relevancy score data.

FIG. 7 is an exemplary customer commitment dashboard 700 illustrating product commitment score (PCS) data displayed according to segmentation. More specifically, the dashboard 700 includes a graphical representation 705 of PCS scores as a function of influence, where the Y-axis includes a range of influence and the X-axis is includes PCS scores which increase from left to right. Thus, the CCF data for a product or service may be visually represented, graphically, according to customer segments, showing the influence of a product or a marketing campaign's effectiveness. A summary paragraph may be provided that describes each particular segment shown in the graph 705. A control panel 710 provides controls which allow the user to select temporal ranges over which data may be viewed. For example, users may select to view PCS scores for segments over a period of a week, a month, a quarter, or a year, or may utilize a pair of date input boxes to provide a bounded range of dates.

FIG. 8 is an exemplary customer commitment dashboard 800 illustrating PCS segmentation and customer journey data. The dashboard 800 includes a table-based representation 805 of segment data, broken down into journey phases. More specifically, each row of the table includes a unique journey phase of a product. Each of the columns of the table include a specific segment. The cells of the table include PCS scores for each journey phase of each segment. It will be understood that while the cells are described as including PCS scores, the cells may be filled alternatively with BCS scores or CRS scores, depending on the CCF data selected by the user. The user may toggle through CCF data scores (e.g., PCS, BCS, and CRS) using tabs 810 a, 810 b, and 810 c.

FIG. 9 is an exemplary customer commitment dashboard 900 illustrating PCS segmentation and segmentation characteristics. The dashboard of FIG. 9 includes a table-based representation 905 of CCF data, where each segment is displayed in row format, while segment characteristics are displayed in column format. Exemplary segment characteristics include influence, PCS score, experience score, and experience change scores. The experience change scores may assist a user in identifying net score changes over time.

FIG. 10 is an exemplary customer commitment dashboard 1000 illustrating a time-based and graphical PCS data analysis. The dashboard of FIG. 10 includes a graphical representation 1005 of PCS scores for various products. PCS scores on the Y-axis are shown over time on the X-axis. A band for highest and lowest PCS scores is overlaid onto the graph, as well as individual PCS scores for the first entity's product, which in this case includes SM2. A trend line is also overlaid onto the graph. Similar graphical representations for competitive position 1010, and driver performance over time 1015 are also shown.

FIG. 11 is an exemplary customer commitment dashboard 1100 illustrating a time-based and graphical analysis of customer journey data. Dashboard 1100 includes a graphical representation of experience scores versus average experience scores within each phase of a customer journey. Exemplary phases include product awareness, connection, evaluation, through to long term commitment. A table representation of experience scores for a plurality of products are provided, as well as a market level analysis of experience scores.

FIG. 12 is an exemplary customer commitment dashboard 1200 illustrating a time-based and graphical analysis of experience scores relative to product awareness. Dashboard 1200 includes similar CCF data relative to the dashboard 1100 of FIG. 11, with an emphasis placed on scores over time. FIG. 13 illustrates an exemplary computing system 1300 that may be used to implement an embodiment of the present technology. The system 1300 of FIG. 13 may be implemented in the contexts of the likes of computing systems, networks, servers, or combinations thereof disclosed herein. The computing system 1300 of FIG. 13 includes one or more processor(s) 1310 and main memory 1320. Main memory 1320 stores, in part, instructions and data for execution by processor(s) 1310. Main memory 1320 may store the executable code when in operation. The system 1300 of FIG. 13 further includes a mass storage device 1330, portable storage medium drive(s) 1340, output devices 1350, input devices 1360, a display system 1370, and peripheral device(s) 1380.

The components shown in FIG. 13 are depicted as being connected via a single bus 1390. The components may be connected through one or more data transport means. Processor(s) 1310 and main memory 1320 may be connected via a local microprocessor bus, and the mass storage device 1330, peripheral device(s) 1380, portable storage device 1340, and display system 1370 may be connected via one or more input/output (I/O) buses.

Mass storage device 1330, which may be implemented with a magnetic disk drive or an optical disk drive, is a non-volatile storage device for storing data and instructions for use by processor(s) 1310. Mass storage device 1330 may store the system software for implementing embodiments of the present technology for purposes of loading that software into main memory 1320.

Portable storage device 1340 operates in conjunction with a portable non-volatile storage medium, such as a floppy disk, compact disk, digital video disc, or USB storage device, to input and output data and code to and from the computing system 1300 of FIG. 13. The system software for implementing embodiments of the present technology may be stored on such a portable medium and input to the computing system 1300 via the portable storage device 1340.

Input devices 1360 provide a portion of a user interface. Input devices 1360 may include an alphanumeric keypad, such as a keyboard, for inputting alpha-numeric and other information, or a pointing device, such as a mouse, a trackball, stylus, or cursor direction keys. Additionally, the system 1300 as shown in FIG. 13 includes output devices 1350. Suitable output devices include speakers, printers, network interfaces, and monitors.

Display system 1370 may include a liquid crystal display (LCD) or other suitable display device. Display system 1370 receives textual and graphical information, and processes the information for output to the display device.

Peripheral device(s) 1380 may include any type of computer support device to add additional functionality to the computer system. Peripheral device(s) 1380 may include a modem or a router.

The components provided in the computing system 1300 of FIG. 13 are those typically found in computer systems that may be suitable for use with embodiments of the present technology and are intended to represent a broad category of such computer components that are well known in the art. Thus, the computing system 1300 of FIG. 13 may be a personal computer, hand held computing system, telephone, mobile computing system, workstation, server, minicomputer, mainframe computer, or any other computing system. The computer may also include different bus configurations, networked platforms, multi-processor platforms, etc. Various operating systems may be used including Unix, Linux, Windows, Macintosh OS, Palm OS, Android, iPhone OS and other suitable operating systems.

It is noteworthy that any hardware platform suitable for performing the processing described herein is suitable for use with the technology. Computer-readable storage media refer to any medium or media that participate in providing instructions to a central processing unit (CPU), a processor, a microcontroller, or the like. Such media may take forms including, but not limited to, non-volatile and volatile media such as optical or magnetic disks and dynamic memory, respectively. Common forms of computer-readable storage media include a floppy disk, a flexible disk, a hard disk, magnetic tape, any other magnetic storage medium, a CD-ROM disk, digital video disk (DVD), any other optical storage medium, RAM, PROM, EPROM, a FLASHEPROM, any other memory chip or cartridge.

While various embodiments have been described above, it should be understood that they have been presented by way of example only, and not limitation. The descriptions are not intended to limit the scope of the technology to the particular forms set forth herein. Thus, the breadth and scope of a preferred embodiment should not be limited by any of the above-described exemplary embodiments. It should be understood that the above description is illustrative and not restrictive. To the contrary, the present descriptions are intended to cover such alternatives, modifications, and equivalents as may be included within the spirit and scope of the technology as defined by the appended claims and otherwise appreciated by one of ordinary skill in the art. The scope of the technology should, therefore, be determined not with reference to the above description, but instead should be determined with reference to the appended claims along with their full scope of equivalents. 

What is claimed is:
 1. A social intelligence system, comprising: means for determining social media participants in at least one phase of a product cycle for a product; means for obtaining social media data from one or more social media platforms for the social media participants relative to the product; means for calculating a product commitment score that represents a commitment level of the social media participants to the product; and means for providing the product commitment score to an end user client device by the social media intelligence system.
 2. The system according to claim 1, wherein the means for calculating the product commitment score comprises a product commitment score module implemented on a computing system and configured for evaluating the social media data by determining keywords included in the social media data that reflect product commitment, the social media data being determined from social media conversations of an author.
 3. The system according to claim 2, wherein determining keywords comprises comparing keywords in the social media data to a matrix of words that reflect any of assess, prefer, and buy behaviors of the author.
 4. The system according to claim 2, wherein the product commitment score module is further configured for computing an author rank for the author, the author rank comprising an analysis of any of social media connections, social status, and combinations thereof, wherein the author rank is associated with an influence for the author.
 5. The system according to claim 4, further comprising computing an adjusted author rank score by dividing the author rank by a sum of author ranks for a plurality of authors, the author rank being one of the plurality of author ranks.
 6. The system according to claim 5, further comprising calculating a component weight for a conversation of the author.
 7. The system according to claim 6, further comprising: determining a product commitment score scaling factor, based upon an analysis of keywords included in the social media conversations; adjusting the scaling factor, such that: the scaling factor for keywords associated with buy behaviors is highest; the scaling factor for keywords associated with prefer behaviors is lower than the scaling factor for keywords associated with buy behaviors; and the scaling factor for keywords associated with assess behaviors is lower than the scaling factor for keywords associated with prefer behaviors.
 8. The system according to claim 7, further comprising multiplying the adjusted author rank with the component weight and the scaling factor to generate the product commitment score.
 9. The system according to claim 2, wherein the author includes a trusted author and evaluating the social media data further includes semiotic analysis of the social media conversations of the author to categorize the social media conversations as being within a product commitment score domain.
 10. A system, comprising: one or more processors; and logic encoded in one or more tangible media for execution by the one or more processors and when executed operable to perform operations comprising: means for determining, via a data gathering module, social media participants in at least one phase of a product cycle for a product; means for obtaining social media data from one or more social media platforms for the social media participants relative to the product; means for evaluating the social media data to categorize social media conversations of the social media data as being within a product commitment score domain; means for calculating a product commitment score that represents a commitment level of the social media participants to the product; and means for providing the product commitment score to an end user client device by the social media intelligence system.
 11. The system according to claim 10, wherein the means for calculating the product commitment score comprises a product commitment score module configured to evaluate the social media data by determining keywords included in the social media data that reflect product commitment, the social media data being determined from social media conversations of an author.
 12. The system according to claim 11, wherein the product commitment score module is further configured to determine keywords by comparing keywords in the social media data to a matrix of words that reflect any of assess, prefer, and buy behaviors of the author.
 13. The system according to claim 12, wherein the product commitment score module is further configured to calculate an author rank for the author, the author rank comprising an analysis of any of social media connections, social status, and combinations thereof, wherein the author rank is associated with an influence for the author.
 14. The system according to claim 13, wherein the product commitment score module is further configured to compute an adjusted author rank score by dividing the author rank by a sum of author ranks for a plurality of authors, the author rank being one of the plurality of author ranks.
 15. The system according to claim 14, wherein the product commitment score module is further configured to a component weight for a conversation of the author.
 16. The system according to claim 15, wherein the product commitment score module is further configured to: determine a product commitment score scaling factor, based upon an analysis of keywords included in the social media conversations; adjust the scaling factor, such that: the scaling factor for keywords associated with buy behaviors is highest; the scaling factor for keywords associated with prefer behaviors is lower than the scaling factor for keywords associated with buy behaviors; and the scaling factor for keywords associated with assess behaviors is lower than the scaling factor for keywords associated with prefer behaviors.
 17. The system according to claim 16, wherein the product commitment score module is further configured to multiply the adjusted author rank with the component weight and the scaling factor to generate the product commitment score.
 18. The system according to claim 11, wherein the author includes a trusted author, and wherein categorizing the social media conversations of the author as being within the product commitment score domain includes semiotic analysis of the social media conversations of the author.
 19. The system according to claim 1, further comprising: means for segmenting social media data into groups of social media conversations based on content of the social media conversations; means for generating a model to predict, for tracking, a group of the segmented social media conversations that will provide accurate and relevant information about a consumer; means for tracking the predicted group of social media conversations based on the generated model; and means for directing merchant resources based on the tracked conversations.
 20. The system according to claim 10, further comprising: means for segmenting social media data into groups of social media conversations based on content of the social media conversations; means for generating a model to predict a group of social media conversations to track to glean the accurate and relevant information about a consumer; means for directing merchant resources based on the generated model. 